






"""simple docstring"""
import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


def 							A						(				__snake_case: Tuple ,     __snake_case: List[str]  )    ->		str:

       """simple docstring"""





       __magic_name__      				=    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
       __magic_name__      				=    Image.open(requests.get(__snake_case ,     stream=__snake_case  ).raw  ).convert('RGB'  )

       __magic_name__      				=    transforms.Compose(
           [
               transforms.Resize((image_size, image_size) ,     interpolation=InterpolationMode.BICUBIC  ),
               transforms.ToTensor(),
               transforms.Normalize((0.48145466, 0.4578275, 0.40821073) ,     (0.26862954, 0.26130258, 0.27577711)  ),
           ]  )
       __magic_name__      				=    transform(__snake_case  ).unsqueeze(0  ).to(__snake_case  )
       return image


def 							A						(				__snake_case: List[Any]  )    ->		Any:

       """simple docstring"""





       if "visual_encoder" in key:
              __magic_name__      				=    re.sub('visual_encoder*' ,     'vision_model.encoder' ,     __snake_case  )
       if "blocks" in key:
              __magic_name__      				=    re.sub(r'blocks' ,     'layers' ,     __snake_case  )
       if "attn" in key:
              __magic_name__      				=    re.sub(r'attn' ,     'self_attn' ,     __snake_case  )
       if "norm1" in key:
              __magic_name__      				=    re.sub(r'norm1' ,     'layer_norm1' ,     __snake_case  )
       if "norm2" in key:
              __magic_name__      				=    re.sub(r'norm2' ,     'layer_norm2' ,     __snake_case  )
       if "encoder.norm" in key:
              __magic_name__      				=    re.sub(r'encoder.norm' ,     'post_layernorm' ,     __snake_case  )
       if "encoder.patch_embed.proj" in key:
              __magic_name__      				=    re.sub(r'encoder.patch_embed.proj' ,     'embeddings.patch_embedding' ,     __snake_case  )

       if "encoder.pos_embed" in key:
              __magic_name__      				=    re.sub(r'encoder.pos_embed' ,     'embeddings.position_embedding' ,     __snake_case  )
       if "encoder.cls_token" in key:
              __magic_name__      				=    re.sub(r'encoder.cls_token' ,     'embeddings.class_embedding' ,     __snake_case  )

       if "self_attn" in key:
              __magic_name__      				=    re.sub(r'self_attn.proj' ,     'self_attn.projection' ,     __snake_case  )

       return key


@torch.no_grad()
def 							A						(				__snake_case: List[str] ,     __snake_case: Optional[Any]=None  )    ->		Optional[Any]:

       """simple docstring"""





       if config_path is not None:
              __magic_name__      				=    BlipConfig.from_pretrained(__snake_case  )
       else:
              __magic_name__      				=    BlipConfig(projection_dim=5_1_2 ,     text_config={} ,     vision_config={}  )

       __magic_name__      				=    BlipForConditionalGeneration(__snake_case  ).eval()

       __magic_name__      				=    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

       __magic_name__      				=    blip_decoder(pretrained=__snake_case ,     image_size=3_8_4 ,     vit='base'  )
       __magic_name__      				=    pt_model.eval()

       __magic_name__      				=    pt_model.state_dict()
       for key in modified_state_dict.copy():
              __magic_name__      				=    modified_state_dict.pop(__snake_case  )
              __magic_name__      				=    rename_key(__snake_case  )
              __magic_name__      				=    value

       hf_model.load_state_dict(__snake_case  )

       __magic_name__      				=    3_8_4
       __magic_name__      				=    load_demo_image(image_size=__snake_case ,     device='cpu'  )
       __magic_name__      				=    BertTokenizer.from_pretrained('bert-base-uncased'  )
       __magic_name__      				=    tokenizer(['a picture of']  ).input_ids

       __magic_name__      				=    hf_model.generate(__snake_case ,     __snake_case  )

       assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

       __magic_name__      				=    hf_model.generate(__snake_case  )

       assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

       if pytorch_dump_folder_path is not None:
              hf_model.save_pretrained(__snake_case  )

       # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
       __magic_name__      				=    (
           'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
       )

       __magic_name__      				=    blip_vqa(pretrained=__snake_case ,     image_size=__snake_case ,     vit='base'  )
       vqa_model.eval()

       __magic_name__      				=    vqa_model.state_dict()
       for key in modified_state_dict.copy():
              __magic_name__      				=    modified_state_dict.pop(__snake_case  )
              __magic_name__      				=    rename_key(__snake_case  )
              __magic_name__      				=    value

       __magic_name__      				=    BlipForQuestionAnswering(__snake_case  )

       hf_vqa_model.load_state_dict(__snake_case  )

       __magic_name__      				=    ['How many dogs are in this image?']
       __magic_name__      				=    tokenizer(__snake_case ,     return_tensors='pt'  ).input_ids

       __magic_name__      				=    hf_vqa_model.generate(__snake_case ,     __snake_case  )
       print(tokenizer.decode(answer[0]  )  )

       assert tokenizer.decode(answer[0]  ) == "[UNK] 1 [SEP]"
       if pytorch_dump_folder_path is not None:
              hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'  )

       __magic_name__      				=    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

       __magic_name__      				=    blip_itm(pretrained=__snake_case ,     image_size=__snake_case ,     vit='base'  )
       itm_model.eval()

       __magic_name__      				=    itm_model.state_dict()
       for key in modified_state_dict.copy():
              __magic_name__      				=    modified_state_dict.pop(__snake_case  )
              __magic_name__      				=    rename_key(__snake_case  )
              __magic_name__      				=    value

       __magic_name__      				=    BlipForImageTextRetrieval(__snake_case  )

       __magic_name__      				=    ['A picture of a woman with a dog sitting in a beach']
       __magic_name__      				=    tokenizer(
           __snake_case ,     return_tensors='pt' ,     padding='max_length' ,     truncation=__snake_case ,     max_length=3_5 ,     ).input_ids

       hf_itm_model.load_state_dict(__snake_case  )
       hf_itm_model.eval()

       __magic_name__      				=    hf_itm_model(__snake_case ,     __snake_case ,     use_itm_head=__snake_case  )
       __magic_name__      				=    hf_itm_model(__snake_case ,     __snake_case ,     use_itm_head=__snake_case  )

       assert out[0].item() == 0.2110687494277954
       assert torch.nn.functional.softmax(out_itm[0] ,     dim=1  )[:, 1].item() == 0.45698845386505127

       if pytorch_dump_folder_path is not None:
              hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'  )


if __name__ == "__main__":
 snake_case : Dict     		=		argparse.ArgumentParser()
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 snake_case : str     		=		parser.parse_args()

 convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""
import argparse
import copy


def 							A						(				__snake_case: Union[str, Any]  )    ->		Tuple:

       """simple docstring"""





       __magic_name__      				=    {}

       with open(__snake_case  ) as f:
              for line in f:
                     if line.split()[0] not in dict_of_neighbours:
                            __magic_name__      				=    []
                            _list.append([line.split()[1], line.split()[2]]  )
                            __magic_name__      				=    _list
                     else:
                            dict_of_neighbours[line.split()[0]].append(
                                [line.split()[1], line.split()[2]]  )
                     if line.split()[1] not in dict_of_neighbours:
                            __magic_name__      				=    []
                            _list.append([line.split()[0], line.split()[2]]  )
                            __magic_name__      				=    _list
                     else:
                            dict_of_neighbours[line.split()[1]].append(
                                [line.split()[0], line.split()[2]]  )

       return dict_of_neighbours


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: str  )    ->		List[str]:

       """simple docstring"""





       with open(__snake_case  ) as f:
              __magic_name__      				=    f.read(1  )
       __magic_name__      				=    start_node

       __magic_name__      				=    []

       __magic_name__      				=    start_node

       __magic_name__      				=    0
       while visiting not in first_solution:
              __magic_name__      				=    1_0_0_0_0
              for k in dict_of_neighbours[visiting]:
                     if int(k[1]  ) < int(__snake_case  ) and k[0] not in first_solution:
                            __magic_name__      				=    k[1]
                            __magic_name__      				=    k[0]

              first_solution.append(__snake_case  )
              __magic_name__      				=    distance_of_first_solution + int(__snake_case  )
              __magic_name__      				=    best_node

       first_solution.append(__snake_case  )

       __magic_name__      				=    0
       for k in dict_of_neighbours[first_solution[-2]]:
              if k[0] == start_node:
                     break
              position += 1

       __magic_name__      				=    (
           distance_of_first_solution
           + int(dict_of_neighbours[first_solution[-2]][position][1]  )
           - 1_0_0_0_0
       )
       return first_solution, distance_of_first_solution


def 							A						(				__snake_case: Union[str, Any] ,     __snake_case: Optional[Any]  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    []

       for n in solution[1:-1]:
              __magic_name__      				=    solution.index(__snake_case  )
              for kn in solution[1:-1]:
                     __magic_name__      				=    solution.index(__snake_case  )
                     if n == kn:
                            continue

                     __magic_name__      				=    copy.deepcopy(__snake_case  )
                     __magic_name__      				=    kn
                     __magic_name__      				=    n

                     __magic_name__      				=    0

                     for k in _tmp[:-1]:
                            __magic_name__      				=    _tmp[_tmp.index(__snake_case  ) + 1]
                            for i in dict_of_neighbours[k]:
                                   if i[0] == next_node:
                                          __magic_name__      				=    distance + int(i[1]  )
                     _tmp.append(__snake_case  )

                     if _tmp not in neighborhood_of_solution:
                            neighborhood_of_solution.append(_tmp  )

       __magic_name__      				=    len(neighborhood_of_solution[0]  ) - 1

       neighborhood_of_solution.sort(key=lambda __snake_case  : x[index_of_last_item_in_the_list]  )
       return neighborhood_of_solution


def 							A						(				__snake_case: List[str] ,     __snake_case: Union[str, Any] ,     __snake_case: List[str] ,     __snake_case: Optional[int] ,     __snake_case: List[str]  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    1
       __magic_name__      				=    first_solution
       __magic_name__      				=    []
       __magic_name__      				=    distance_of_first_solution
       __magic_name__      				=    solution

       while count <= iters:
              __magic_name__      				=    find_neighborhood(__snake_case ,     __snake_case  )
              __magic_name__      				=    0
              __magic_name__      				=    neighborhood[index_of_best_solution]
              __magic_name__      				=    len(__snake_case  ) - 1

              __magic_name__      				=    False
              while not found:
                     __magic_name__      				=    0
                     while i < len(__snake_case  ):
                            if best_solution[i] != solution[i]:
                                   __magic_name__      				=    best_solution[i]
                                   __magic_name__      				=    solution[i]
                                   break
                            __magic_name__      				=    i + 1

                     if [first_exchange_node, second_exchange_node] not in tabu_list and [
                         second_exchange_node,
                         first_exchange_node,
                     ] not in tabu_list:
                            tabu_list.append([first_exchange_node, second_exchange_node]  )
                            __magic_name__      				=    True
                            __magic_name__      				=    best_solution[:-1]
                            __magic_name__      				=    neighborhood[index_of_best_solution][best_cost_index]
                            if cost < best_cost:
                                   __magic_name__      				=    cost
                                   __magic_name__      				=    solution
                     else:
                            __magic_name__      				=    index_of_best_solution + 1
                            __magic_name__      				=    neighborhood[index_of_best_solution]

              if len(__snake_case  ) >= size:
                     tabu_list.pop(0  )

              __magic_name__      				=    count + 1

       return best_solution_ever, best_cost


def 							A						(				__snake_case: Union[str, Any]=None  )    ->		Tuple:

       """simple docstring"""





       __magic_name__      				=    generate_neighbours(args.File  )

       __magic_name__				,  __magic_name__      				=    generate_first_solution(
           args.File ,     __snake_case  )

       __magic_name__				,  __magic_name__      				=    tabu_search(
           __snake_case ,     __snake_case ,     __snake_case ,     args.Iterations ,     args.Size ,     )

       print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""  )


if __name__ == "__main__":
 snake_case : str     		=		argparse.ArgumentParser(description="""Tabu Search""")
 parser.add_argument(
     """-f""",
     """--File""",
     type=str,
     help="""Path to the file containing the data""",
     required=True,
 )
 parser.add_argument(
     """-i""",
     """--Iterations""",
     type=int,
     help="""How many iterations the algorithm should perform""",
     required=True,
 )
 parser.add_argument(
     """-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
 )

 # Pass the arguments to main method
 main(parser.parse_args())